Report Summary

Exploitation of remotely sensed imagery for environmental mapping and monitoring

Techniques for exploiting complex remotely sensed imagery for environmental mapping and ecosystem monitoring purposes have been developed and improved. The techniques included: combination and effective use of multisensor multisatellite imagery; development of robust image classification techniques; integration of neural and statistical image analysis methods; effective exploitation of ancillary and contextual information; utilization of methods from machine vision in remote sensing. Experiments were carried out on using integrated data from conventional optical and infrared sensors and radar imagery. These experiments demonstrated that through the use of integrated coincident Landsat thematic mapper (TM) scenes and earth remote sensing satellite (ERS) synthetic aperture radar (SAR) scenes it was possible to obtain considerable improvements in the visual quality and absolute accuracy of derived land cover products. In order to use the integrated datasets, procedures were put in place for operationally coregistering Landsat TM images and ERS-1 SAR images. A practical technique was also implemented for removing speckle noise from the SAR imagery by making use of a multidimensional regression approach based on the coincident Landsat imagery. Experiments were also conducted on integrating neural network and statistical classifiers. It was found that significant gains could be achieved in classification accuracy. This takes advantage of the fact that in a typical classification problem, some classes are well modelled by statistical distributions whereas others are better modelled by a semilinear approach as implemented in multilayer perceptron neural networks. In order to demonstrate the additional discrimination power of combined sensor data, an experimental test was carried out to map the diversity of forest species in Portugal. The experiment showed that integrated TM and SAR data could be used to map 8 separate forest classes with an average of 80% accuracy including both broad leaved and coniferous varieties.